Embodiments relate to detecting stress in an individual and, more particularly, to a method and system for continuous classification of physiological stress in a mobile environment.
Though mobile health applications and wearable physiological sensors have the potential to analyze and present meaningful data to better manage and optimize general health and specific health conditions of an individual, such applications currently simply collect data and produce readouts of the collected data. For example, wearable devices are now able to track fitness-related metrics, such as, but not limited to, distance walked or run, calorie consumption, heartbeat rate, and quality of sleep.
Wearable physiological sensors provide quantifiable data in real time that may correlate with stress (such as heart rate variability and electrodermal activity), but these sensors are also activated by other inputs such as temperature and physical activity. Individual differences (e.g., age, gender, health status) and daily activities (e.g., physical movements, environmental changes) pose a complex problem in achieving an accurate classifier for stress.
There is a growing need to support the classification of physiological and psychological states when an individual is in a natural environment, as opposed to a controlled environment, such as an examination room or laboratory. Detecting and addressing stress is a key measure for mobile health applications, but the main challenge in addressing this need is an inability to classify stress in a mobile environment in real time. Current state of the art methods for stress monitoring are laboratory-based (not mobile) and episodic in nature (e.g. self report). Therefore individuals would benefit from a system and method that can discriminate between physiological stress and other (normal) psychological states of the user to provide for an accurate, quantitative classifier for continuous and objective real-time stress assessment.